In order to measure the performance of a building's processes over time, a large amount of data needs to be collected and analyzed. Previously used solutions include compiling point logs that list data points and their current values at various intervals. The analysis of trend data has generally been limited to single points of data associated with a status (such as an “alarm” condition, “clear” condition, “failure” condition, etc.) or multi-point trend data without a listed status. For each data point, graphs are generated for personnel to review in order to attempt to detect trends in the data.
However, attempting to analyze performance trends of a single process, much less an entire building's many processes, may prove to be very time consuming and/or cost-prohibitive with existing methods. Additionally, current methods do not provide a way to easily analyze the collected data across processes and systems, or determine trends in a way to easily identify service actions that can be performed to improve process performance.